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In the context of carbon neutrality target, renewable energy sources have been transforming from "supplementary energy" to "main energy", which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the "resource-environment-demand" comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry. • This study examines the impact of high-dimensional features variables on urban solar energy development. • Over 70% of solar PV installation capacity is concentrated in Northern and Northwest parts of China. • Water resources, solar radiation, population density have significant impact on solar PV distribution. • Solar PV distribution in China should shift from resource orientation to the comprehensive orientation. [ABSTRACT FROM AUTHOR]

Rapid developments in environmental infrastructure have contributed to significant improvements in green total factor productivity, but further investigation is required to provide a detailed assessment to understand the policy mechanisms involved. This paper analyzes environmental progress in China through MMQR, CCEMG, and AMG as empirical strategies for 30 provinces in China. Our empirical results reveal that energy optimization through renewable energy is the most effective channel to improve green total factor productivity, though it is not the only available option. Since environmental regulations, infrastructure development, and green technology innovation also directly impact energy efficiency, adopting these within policy channels will positively impact environmental sustainability. Our empirical approach helps suggest novel environmental policy suggestions. In particular, policymakers must introduce structural changes within energy developments to foster renewable energy. Furthermore, China must increase environmental spending to upgrade its energy infrastructure further and solve ecological issues. These insights offer valuable policy guidance for decision-makers in China and globally, aiming to foster economic and environmental sustainability and achieve zero-carbon transition goals. • The current study analyses the role of green factor productivity in zero carbon transition. • We use 30 chinese provincial datasets from 2005 to 2020. • Energy transition is the most effective channel to improve green total factor productivity. [ABSTRACT FROM AUTHOR]

Renewable energy (RE) plays a crucial role in global energy transformation, and a thorough study of the potential impact of RE on regional carbon emissions is of great significance. This is particularly relevant to China, which needs to clarify its path to carbon reduction. Using the sample data of 30 provinces in China from 2000 to 2021, this paper uses the Granger causality test to verify the causal relationship between carbon emission intensity (CEI) and other factors. It builds a mediation effect model on this basis to explore the direct impact effect and indirect transmission path of renewable energy utilization (REU) on CEI. The results show that REU has a one-way causal relationship with CEI. REU can directly and indirectly reduce CEI by improving social wealth and changing the direction of energy investment. In addition, REU indirectly increases CEI through the transmission paths of investment in the energy industry - social affluence and industrial level-social affluence. The CEI is indirectly reduced through the conduction paths of (social affluence-Urbanization rate), (Investment in the energy industry-Urbanization rate), (Industrial level-Urbanization rate), and (Industrial level-Investment in the energy industry). These conclusions will assist policymakers in exploring targeted pathways for low-carbon power development, providing a reference for strategic and sustainable carbon reduction policies. [Display omitted] • Enrich the view of renewable energy's indirect impact on carbon emissions. • Renewable energy's carbon emission reductions go beyond replacing fossil fuels. • Constructed a mediating effect model between renewable energy and carbon emissions. • Renewable energy can promote or inhibit carbon emissions through various pathways. • Research helps develop more effective renewable energy sustainability strategies. [ABSTRACT FROM AUTHOR]

The widespread implementation of feed-in tariff (FIT) policies has played a crucial role in fostering the development of wind power, with their positive effects firmly established in numerous studies. However, the impact of regionally differentiated FIT policies on the misallocation of wind power resources remains a topic of contention, with limited research dedicated to this area. This paper aims to address this gap by examining the implications of such policies on the intensive and extensive margins of wind power installed capacity in China, shedding light on the underlying mechanisms driving resource misallocation. Empirical findings indicate that, concerning the intensive margin, the policy amplifies the concentration of wind power investments in regions characterized by abundant wind resources but low electricity demand. These regions present favorable conditions for large-scale wind farms with cost advantages, consequently exacerbating the misallocation of wind power resources. However, on the extensive margin, the policy promotes the likelihood of locating small and medium-sized wind farms in regions with poor wind resources but higher tariff rates, thus partially mitigating resource misallocation. In summary, China's policy hampers wind power investments in regions characterized by high electricity demand but limited wind resources. This suggests that the negative impact on the intensive margin outweighs the positive impact on the extensive margin. The findings of this study bear significant implications for the development of renewable energy support policies, particularly in countries grappling with substantial regional disparities in renewable energy resources. • We cope with the controversial effect of FIT policies on the resource misallocation • We provide novel perspectives as the intensive and extensive margins • China's regionally differentiated FIT policy hampers wind power investments • We provide implications for countries implemented regionally differentiated FIT [ABSTRACT FROM AUTHOR]

Green power trading is an institutional innovation proposed by China to promote green and sustainable development. This policy aims to relieve the serious debt pressure of renewable energy generation enterprises, thus laying the foundation for achieving carbon reduction targets. This paper empirically examines the role of green power trading by constructing the difference-in-differences (DID) model. Our findings indicate that green power trading significantly reduces the debt levels of policy-covered generation enterprises. This effect is more pronounced for state-owned enterprises, companies with better ESG performance, companies in regions with higher degree of marketization and companies specializing in renewable energy power generation. Further research discusses the impact mechanism of green power trading in terms of external and internal environmental channels. We find that a strong green development atmosphere, especially the external atmosphere, will significantly reinforce the weakening effect of green power trading on corporate debt. Our research provides empirical evidence for the success of green power trading policy in its pilot phase. • Green power trading significantly alleviates the policy-covered firms' debt burden. • The role of green power trading presents heterogeneity in enterprise characteristics. • A better regional environment for green growth can stimulate the effect of green power trading. [ABSTRACT FROM AUTHOR]

China has begun a new round of electricity market reform since 2015, aiming to transform the regulated form into a competitive one. Since the generation cost of clean energy is still relatively high compared to coal-fired power plants, the major source of the energy sector in China, the environmental outcomes from market reform would remain uncertain. Therefore, green policies play a critical role during the process of reform and hence would determine whether China can achieve the goal of carbon-neutral in 2060. In light of these concerns, this paper performed a simulation study based on the agent-based model (ABM) to analyze the market performance and environmental conditions after reform. Our results demonstrate that given current cost disadvantages, the market share will flow to coal-fired power plants in the competitive electricity market without the support of green policies, resulting in the increase of CO2, NOX, SO2, and smoke emissions by 4.85 %, −1.7 %, 6.48 %, and 6.86 %. Further scenario analysis shows that to improve such environmental outcomes, policies should focus on pushing the replacement of coal-fired energy, either by renewable or gas energy. The simulation results of green policy scenarios demonstrate significant co-benefits, that carbon and other pollution emissions will decrease simultaneously. Despite the environmental gain from green policies, results of the policy efficiency analysis indicates that the FIT on renewable energy is the least effective, while pollution tax turns to be the most effective. Our results imply that green policies should be well considered when constructing the competitive electricity market in China, to effectively achieve both the national carbon-neutral and local environmental goals. • Green policies should be well designed during electricity market reform. • The environmental condition may get worse without green policies. • Green policies in the electricity market in China should focus on the replacement of coal-fired energy. • Green policies demonstrate significant environmental co-benefits on carbon and pollution control in the energy sector. • Pollution tax in the electricity market shows the highest efficiency. [ABSTRACT FROM AUTHOR]

The diffusion of renewable energy sources (RES) is a fundamental objective of the worldwide policy actions for sustainable development, at the UN level with the sustainable development goals (SDG) recommendations, to ensure access to affordable, reliable, sustainable and modern energy for all (SDG 7). Also, primary attention to RES has been given at the EU level with the new Green Deal and the new objectives of the Next Generation EU after the Covid pandemic, and at the level of national Governments worldwide. So far, there has not been an analysis of the RES convergence process across countries worldwide, given that the issue of climate change is a global externality. Previous analyses have focused on specific regions, such as EU, OECD, provinces of China. This paper fills this gap, providing new evidence on the convergence process of RES for the 176 countries that account for more than 98% of the world population, from 1990 to 2018. A common panel data set has been used to take into account countries' specific effects. Several socio-economics and political variables are introduced to test conditional convergence such as openness to trade, developments in financial markets, income distribution, level of education. The results of this new contribution reveal that there is evidence of sigma-absolute and conditional beta-convergence process for several groups of countries. Moreover, the conditional convergence analysis shows that spatial spillover effects exert rich and complex impact on convergence speed. Finally, we provide policy recommendations, highlighting that the decarbonization target in 2050 needs additional mobilization of public and private resources to pursue a common, convergence path worldwide. • We analyze the renewable energy convergence process across countries worldwide. • We analyses the catching-up measuring the gap between benchmark and non-benchmark. • We use variables RES as percentage of electricity production and energy consumption. • We find that heterogeneity exists in the worldwide RES convergence process. • A possible line for future research could be to disaggregate renewable sources. [ABSTRACT FROM AUTHOR]

This paper describes the methods used to evaluate the potential for achieving sustainable energy development in six Chinese villages included in the Sustainable Users’ Concepts for China Engaging Scientific Scenarios (SUCCESS) Project by examining energy efficiency potential and local renewable energy prospects. The approaches needed to obtain and analyse information on possible energy efficiency measures and renewable energy resources are summarised. Results are presented in terms of cumulative net savings in primary energy consumption, as an indicator of energy resource depletion, and associated carbon dioxide emissions, as an indicator of global climate change. Options for sustainable energy development are ranked in order of likely implementation and practical actions which could be considered in each village are identified. [Copyright &y& Elsevier]